What does adjusted cycle length mean

We also acknowledge the potential for human error in identification of the start of the cycle, the start and peak of the LH surge and the BBT rise based on self-reported bleeding, urinary LH test results and temperature measurements respectively. Study participants were able to purchase approved LH tests from the app developers, however, it is known that some users prefer to buy other commercially available tests between which there may be small variations in LH threshold values for a positive result.

Given the variations in cycle length and follicular phase length that we have described, especially for cycles outside the average range 25—30 days , an individualised approach to identification of the fertile window should be adopted. There are more than fertility tracking apps freely available for download. Many of these apps claim to identify fertile days based on traditional assumptions about key menstrual cycle parameters such as regularity of cycle length, follicular phase length and luteal phase length.

Apps giving predictions of fertile days based solely on an outdated understanding of ovulation day variation could completely miss the fertile window. It is, therefore, unsurprising that several studies have shown that calendar apps are not accurate in identifying the fertile window.

Some fertility apps are based on sophisticated algorithms for individualised identification of the fertile window relying on physiological parameters such as BBT which are more acceptable for large numbers of women.

The addition of BBT and the use of a fertility app may help to narrow down testing days and therefore be more convenient and cheaper. Individualised identification of the fertile window based on BBT and menstruation dates can help to reduce the time to conception in some cases. With women globally delaying fertility 39 the potential value of fertility tracking apps as a platform for delivery of individualised fertility education and preconception care should not be underestimated.

Anecdotally there is poor understanding of fertility amongst the general population, which can lead to both unintended pregnancies and delayed time to conception with associated psychological suffering for those wishing to start a family. The value of fertility apps as educational platforms to achieve public health benefits through standardised health promotion messages during key stages of reproductive life such as preconception, pregnancy and birth spacing is also being explored.

Finally, the widespread use of mobile phone apps for personal health monitoring is generating large amounts of data on the menstrual cycle. Provided that the real-world data can be validated against traditional clinical studies done in controlled settings, there is enormous potential to uncover new scientific discoveries.

This is one of the largest ever analyses of menstrual cycle characteristics. These initial results only scratch the surface of what can be achieved. We hope to stimulate greater interest in this field of research for the benefit of public health.

Physiological data, including daily BBT sublingual measurement , cycle by cycle dates of menstruation, and urinary LH test results, were collected prospectively from users of the Natural Cycles app. Participant characteristics including age and BMI were determined through mandatory in-app questions that must be completed during the sign-up process. Users are recommended to measure their temperature on 5 out of 7 days per week as soon as they wake up. They are requested to report whether a temperature measurement may be deviating for reasons such as disrupted sleep or alcohol consumption the night before.

The algorithm also identifies deviating temperatures if the value is outside the range All users in the study had consented at registration to the use of their data for the purposes of scientific research and could remove their consent at any time.

A surge in LH is responsible for triggering follicle rupture. At the onset of menses, marking the start of the follicular phase, the corpus luteum collapses and progesterone levels fall back to a low level until the next preovulatory increase.

Progesterone has a thermogenic effect so its levels can be tracked by measuring BBT. BBT is at a relatively constant low level during the follicular phase, reaching its lowest level the nadir prior to ovulation, 43 and then displays a distinct rise of 0.

The algorithm within the app detects ovulation retrospectively based on BBT measurements, menstrual cycle parameters and additionally on positive urinary LH tests. The algorithm can identify the BBT rise associated with ovulation in the presence of measurement errors, missing data and BBT rise occurring over a variable length of time.

The horizontal grey line is the cover line. Comparisons are made using standard statistical techniques taking into account sample size and standard deviation. If ovulation is not detected in this initial test then more tests are performed with a rolling average over an increasing number of days up to 1 week. If a positive-LH test has been recorded, fewer high temperatures are required in order to detect ovulation since the LH test provides extra confidence that ovulation has occurred. The app recommends which days to take an LH test, considering the uncertainty of the ovulation day such that it minimises the number of LH tests used while ensuring that the user will not miss her surge.

For users on Plan mode the app always recommends which days to check for LH since Plan users are in general more keen on finding the surge, even if it requires a large number of LH tests. The app will, however, only recommend to start checking LH 10 days prior to the earliest recorded ovulation day even if the total uncertainty is larger. As the LH surge typically lasts for several days 42 the probability of missing the surge if only testing every other day is relatively small.

The app, therefore, recommends to only test every other day until close to the expected ovulation day. If one positive LH test has been entered, but no positive or negative LH test entry exists on the day immediately before, then the user is encouraged to test the following day to establish whether the positive test corresponds to the first or second day of the surge.

If no such test is entered, the app assumes the first LH test marks the first day of the surge. Cycles in which ovulation has been detected are hereafter referred to as ovulatory cycles. If ovulation has been detected in the current cycle then the algorithm selects the most suitable candidate day to call the First High Point FHP using a system of measurements based on comparisons of each temperature to the phase averages.

This is the day on which the temperatures immediately before and after are most consistent with the follicular and luteal phase averages respectively. On average the FHP temperature is just below the cover line.

In a previous study the FHP was 1. An evaluation of the timing of the FHP and the LH peak relative to the data of Ecochard et al is available in Supplementary materials.

This means that ovulation itself is estimated to occur on the day of the last low temperature before the rise as suggested by Hilgers and Bailey 46 and Mouzon et al. Another marker besides the BBT shift that has been used in clinical settings to estimate the day of ovulation is the day of luteal transition DLT defined as the ratio of oestrogen to progesterone falling below a critical threshold.

Women using the app who had registered between 1st September and 1st February , had given their consent for the use of their data in research, were aged 18—45 at registration, had a BMI between 15 and 50 and had not been using hormonal contraception within the 12 months prior to registration were included.

Users who stated at registration that they had a PCOS hypothyroidism or endometriosis or who had menopausal symptoms were excluded. They were required to have logged at least ten nondeviating temperatures. Figure 7 summarises the number of users and cycles at each step of the selection process. Users are instructed not to log very light bleeding just before the period as bleeding but to wait until the flow increases. The follicular phase was defined as the first day of recorded menstruation to the EDO.

Luteal phase length was defined as the day after the EDO to the day before the next day of recorded menstruation. We calculated mean cycle length, duration of bleeding bleed length , follicular phase length and luteal phase length in ovulatory cycles. The following cohort splits by cycle length were defined: very short cycles 15—20 days , short cycles 21—24 days , medium cycles 25—30 days , long cycles 31—35 days and very long cycles 36—50 days.

We calculated the same statistics as well as per-user cycle length variation for cohorts of ovulatory cycles by user age at registration 18—24, 25—29, 30—34, 35—39 and 40—45 years and BMI 15— We also calculated the mean proportion of ovulatory cycles as a fraction of all cycles recorded by the user in each of the age and BMI cohorts.

Owing to the very large sample sizes in this study, P values were not calculated since they can be very small even if differences between cohorts are of no clinical significance.

Further information on research design is available in the Nature Research Reporting Summary linked to this article. The data that support the findings of this study are available from Natural Cycles Nordic AB but restrictions apply to the availability of these data, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the developers. The code that constitutes the mobile application including the ovulation detection algorithm is commercially sensitive and not available for release.

The code used to analyse the database of recorded cycles may be made available upon reasonable request to the corresponding author and with permission of the company. Wilcox, A. Reed, B. The normal menstrual cycle and the control of ovulation. De Groot, L. Vollman, R. The menstrual cycle. Major Probl. Google Scholar.

Fehring, R. Variability in the phases of the menstrual cycle. Neonatal Nurs. Article Google Scholar. Mihm, M. The normal menstrual cycle in women. Treloar, A. Variation of the human menstrual cycle through reproductive life. Lenton, E. Normal variation in the length of the luteal phase of the menstrual cycle: identification of the short luteal phase. BJOG 91 , — Liu, Y. Factors affecting menstrual cycle characteristics.

Chiazze, L. The length and variability of the human menstrual cycle. Rowland, A. Influence of medical conditions and lifestyle factors on the menstrual cycle. Epidemiology 13, — Harlow, S.

The association between weight, physical activity, and stress and variation in the length of the menstrual cycle. Menstruation and menstrual disorders: the epidemiology of menstruation and menstrual dysfunction.

Goldman, M. Dreaper, J. Women warned about booming market in period tracker apps—BBC News. Berglund Scherwitzl, E. Perfect-use and typical-use Pearl Index of a contraceptive mobile app. Jennings, V. Perfect- and typical-use effectiveness of the Dot fertility app over 13 cycles: results from a prospective contraceptive effectiveness trial.

Health Care 1—6. Peragallo Urrutia, R. Effectiveness of fertility awareness—based methods for pregnancy prevention. Duane, M. The performance of fertility awareness-based method apps marketed to avoid pregnancy. Board Fam. Lundberg, O. Abstracts of the 34rd annual meeting of the european society of human reproduction and embryology. Simmons, R. Assessing the efficacy of an app-based method of family planning: the Dot Study Protocol.

JMIR Res. Identification and prediction of the fertile window using natural cycles. Health Care 20 , — Shilaih, M. Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle.

Regidor, P. Identification and prediction of the fertile window with a new web-based medical device using a vaginal biosensor for measuring the circadian and circamensual core body temperature. Starling, M. User profile and preferences in fertility apps for preventing pregnancy: an exploratory pilot study.

Behre, H. Liu, B. Symul, L. Assessment of menstrual health status and evolution through mobile apps for fertility awareness. Fertility awareness-based mobile application for contraception. Health Care 21 , — Baird, D. Application of a method for estimating day of ovulation using urinary estrogen and progesterone metabolites. Epidemiology 6 , — Bellver, J.

Obesity and assisted reproductive technology outcomes. Online 12 , — Meldrum, D. Aging and the environment affect gamete and embryo potential: can we intervene?

Panth, N. The influence of diet on fertility and the implications for public health nutrition in the United States. Block, J. Population trends and variation in body mass index from to in the Framingham Heart Study Offspring Cohort.

Inoue, Y. Epidemiology of obesity in adults: latest trends. O besity: Preventing and Managing the Global Epidemic. Setton, R. The accuracy of web sites and cellular phone applications in predicting the fertile window.

Freis, A. Plausibility of menstrual cycle apps claiming to support conception. Public Health 6 , 98 Sohda, S. Relationship between the menstrual cycle and timing of ovulation revealed by new protocols: analysis of data from a self-tracking health app. Internet Res. Johnson, S. Can apps and calendar methods predict ovulation with accuracy?

Harper, J. The need to improve fertility awareness. Online 4 , 18—20 Cousineau, T. Psychological impact of infertility. Torous, J. Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements. Based Ment. Health 21 , — Godbert, S. These variations are usually normal and healthy. In some cases though, they can point to something more serious, like a medical condition that needs your attention.

Clinically speaking, cycles are described in two ways: regular and irregular. This refers to a cycle's length, and how much cycle length varies, cycle-to-cycle.

There are also regular and irregular ranges for menstrual bleeding, and regular and irregular ranges for pain. A period that comes every cycle at exactly the same time is not the norm. But even outside of those times, slight variations in timing and symptoms are common. If you are stressed out during the first half of your cycle, for example, your ovulation may happen a couple of days later than usual. This will cause certain symptoms such as sore breasts to happen later as well.

Your period will also then be a couple of days late 1. They can be caused by changes in your environment, behavior, or health, including things like diet and exercise, sleep changes and jetlag and smoking 3,4. Slight changes in your cycle length, period length, and period volume are also normal over time 1. Menstrual cycles are caused by the rhythmic ups and downs of your reproductive hormones, and the physical changes those ups and downs cause.

They trigger the growth of follicles in the ovaries, the release of an egg ovulation and the growth and shedding of the uterine lining the period. The reproductive hormones include estrogen, progesterone, follicle stimulating hormone, luteinizing hormone, testosterone and others.

In a way, hormones in the menstrual cycle act a bit like they are in a relay race. As the cycle moves forward, one hormone often triggers the next, which then triggers the next, moving the cycle through its different phases. Having more or less of certain hormones will create changes in the pace and timing of the cycle. Regular mid-cycle spotting may be caused by ovulation 1. Any unpredictable spotting or continual changes to period length should be addressed with a healthcare provider 8.

If the majority of your cycles fall outside these ranges, read more here. Variations that are more significant also occur. They are often temporary, lasting only one or two cycles. These can happen for reasons such as an undetected miscarriage, high stress, or not getting enough calories. The cycle and period following that one may also be a bit different. Temporary irregularities in the menstrual cycle are usually nothing to worry about 1,2,9.

But irregular cycles can also be longer lasting. Long-term irregularities can happen in response to things such as working night shifts and high-intensity exercise or due to medical conditions such as polycystic ovary syndrome 3,8,9, Many people have undiagnosed medical conditions which affect their cycle Periods that are very heavy , or painful may also signal an issue—endometriosis, for example, is a common and underdiagnosed cause of painful menstruation.

Reproductive hormones play a role well beyond reproduction. They affect everything from your sleep, mental health and weight to your bone density and heart health